The first network that was built was trained to distinguish
between the House and Purple finches. These birds were chosen due to the
similarity of their calls, and the variation of the vocalizations for each
species observed within the data sets. The next figure shows a comparison of
the frequency spectrum of the vocalizations of these two species.

The network that was built consisted of a single hidden
layer, between and input and output layers. The input layer supplied the
twenty-five vector elements as inputs. The hidden and output layers both
contained four and two neurons respectively, and used hyperbolic tangent
as their discriminating function. This network formed the basis for all
further testing. This simple network trained very quickly and provided one
hundred percent accurate categorization.